Results 151 to 160 of about 18,989 (190)

M-FCN: Effective Fully Convolutional Network-Based Airplane Detection Framework

IEEE Geoscience and Remote Sensing Letters, 2017
Airplane detection is a challenging problem in complex remote sensing imaging. In this letter, an effective airplane detection framework called Markov random field-fully convolutional network (M-FCN) is proposed. The M-FCN uses a cascade strategy that consists of an FCN-based coarse candidate extraction stage, a multi-Markov random field (multi-MRF ...
Yiding Yang, Yin Zhuang, Fukun Bi, Hao Shi, Yizhuang Xie   +4 more
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A fully convolutional networks (FCN) based image segmentation algorithm in binocular imaging system

2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 2018
This paper proposes an image segmentation algorithm with fully convolutional networks (FCN) in binocular imaging system under various circumstance. Image segmentation is perfectly solved by semantic segmentation. FCN classifies the pixels, so as to achieve the level of image semantic segmentation.
龙邹荣 Long Zourong, Biao Wei, Feng Peng, 俞鹏炜 Yu Pengwei, Yuanyuan Liu   +4 more
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A fully convolutional network (FCN) based automated ischemic stroke segment method using chemical exchange saturation transfer imaging

Medical Physics, 2022
AbstractBackgroundChemical exchange saturation transfer (CEST) MRI is a promising imaging modality in ischemic stroke detection due to its sensitivity in sensing postischemic pH alteration. However, the accurate segmentation of pH‐altered regions remains difficult due to the complicated sources in water signal changes of CEST MRI.
Yingcheng Zhao, Yibing Chen, Yanrong Chen, Lihong Zhang, Xiaoli Wang, Xiaowei He   +5 more
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RR-FCN: Rotational Region-Based Fully Convolutional Networks for Object Detection

, 2018
In this paper, we present rotational region-based fully convolutional networks (RR-FCN) for object detection. In contrast to previous detectors that do not consider rotation, our region-based detector incorporates rotational invariance into networks efficiently and generate more appropriate features according to the rotation angle.
Dingqian Zhang, Hui Zhang, Haichang Li, Xiaohui Hu   +3 more
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HF-FCN: Hierarchically Fused Fully Convolutional Network for Robust Building Extraction

, 2017
Automatic building extraction from remote sensing images plays an important role in a diverse range of applications. However, it is significantly challenging to extract arbitrary-size buildings with largely variant appearances or occlusions. In this paper, we propose a robust system employing a novel hierarchically fused fully convolutional network (HF-
Tongchun Zuo, Juntao Feng, Xuejin Chen
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Glioblastomas brain Tumor Segmentation using Optimized U-Net based on Deep Fully Convolutional Networks (D-FCNs)

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2020
Manual segmentation during clinical diagnosis, is considered as time-consuming and depend to the neuroradiologists level of expertise, however due to the large spatial and structural variability of brain tumors in shapes and sizes besides to the tumor sub-region voxels’high in-homogeneity could make a reliable and accurate and automated segmentation a ...
Hiba Mzoughi, Ines Njeh, Mohamed Ben Slima, Ahmed BenHamida   +3 more
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Fully convolutional network (FCN) model to extract clear speech signals on non-stationary noises of human conversations for cochlear implants

2017 IEEE MIT Undergraduate Research Technology Conference (URTC), 2017
Cochlear implant (CI) electronically stimulates the nerve to help those with severe hearing lost. However, under noisy backgrounds, speech perception tasks have remained difficult for CI users. Therefore, speech enhancement (SE) is a critical component to improve speech perception examining through different noise scenarios. In this study, we developed
Tsai Yi-Ting, Lauren D. Liao
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Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance images

Australasian Physical & Engineering Sciences in Medicine, 2018
Segmentation of colorectal tumors is the basis of preoperative prediction, staging, and therapeutic response evaluation. Due to the blurred boundary between lesions and normal colorectal tissue, it is hard to realize accurate segmentation. Routinely manual or semi-manual segmentation methods are extremely tedious, time-consuming, and highly operator ...
Junming Jian, Fei Xiong, Wei Xia, Rui Zhang, Jinhui Gu, Xiaodong Wu, Xiaochun Meng, Xin Gao   +7 more
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NB-FCN: Real-Time Accurate Crack Detection in Inspection Videos Using Deep Fully Convolutional Network and Parametric Data Fusion

IEEE Transactions on Instrumentation and Measurement, 2019
For the safe operations of nuclear power plants, it is important to inspect the reactor internal components frequently. However, current practice involves human technicians who review the inspection videos and identify cracks on metallic surfaces of underwater components, which is costly, time-consuming, and subjective.
Fu‐Chen Chen, Mohammad R. Jahanshahi
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DSMS-FCN: A Deeply Supervised Multi-scale Fully Convolutional Network for Automatic Segmentation of Intervertebral Disc in 3D MR Images

, 2018
This paper addresses the challenging problem of segmentation of intervertebral discs (IVDs) in three-dimensional (3D) T2-weighted magnetic resonance (MR) images. We propose a deeply supervised multi-scale fully convolutional network for segmentation of IVDs in 3D MR images.
Guodong Zeng, Guoyan Zheng
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